Device for recommending location in building by using fingerprint of access point, and method using same

ABSTRACT

Disclosed are a device for recommending locations in a building using fingerprints of access points and a method using the device. The locations of access points are detected by collecting fingerprints for the access points from terminals of multiple users, the locations of stores in the building are detected by using the fingerprints and payment histories of the multiple users, and locations may be recommended to a recommendation target user based on a meta-path, using a metamap generated by integrating the locations of the access points and stores with the indoor plan of the building. Indoor positioning is enabled by detecting the locations of access points using the information obtained from the terminals of multiple users, whereby constructing infrastructure information for the access points in the building in advance may not be required.

CROSS REFERENCE TO RELATED APPLICATION

This is a Continuation Application of a U.S. patent application Ser. No.14/896,908, filed on Dec. 8, 2015 which is a National Phase ofPCT/KR2014/010255, filed Oct. 29, 2014, which is based on and claimspriority of Korean Patent Application No. 10-2014-0048058 filed on Apr.22, 2014 in the Korean Intellectual Property Office. The disclosures ofthe above-listed application are hereby incorporated herein by referencein their entirety.

TECHNICAL FIELD

The present invention generally relates to a device for recommendinglocations in a building using fingerprints of access points and a methodusing the device, which recommend locations to a user based oninformation collected from multiple user terminals even if there is noinformation about the locations of the access points in the building.More particularly, the present invention relates to a device forrecommending locations in a building using fingerprints of access pointsand a method using the device, which detect the locations of the accesspoints and stores in the building by collecting fingerprints for theaccess points installed in the building from multiple user terminals,and may recommend locations to a recommendation target user using ametamap generated by integrating the locations of the access points andstores in the building with the indoor plan of the building.

BACKGROUND ART

In the field of indoor positioning, efforts to improve precision byprocessing various signals including infrared rays, ultrasonic waves,magnetic fields, Wi-Fi, vision, and the like, have been continued. Assmart phones in which Wi-Fi modules are embedded are widely used, theeasiest approach for indoor positioning is a method using fingerprintsof Wi-Fi Access Points (APs).

The fingerprinting method generates an indoor magnetic field map inadvance and estimates a location by comparing a value measured by thegeomagnetic sensor of a smart phone with the information in the map.According to Bell Laboratories in the U.S., such a fingerprinting methodbased on Wi-Fi has high precision, with a position error of only 1 to 2m. However, maintenance is expensive, and localization is slow becausethe size of the map to be generated is very large.

Most conventional indoor positioning techniques require the constructionof infrastructures in advance. In other words, the locations of Wi-FiAPs in a building must be known, and without such information, neitherthe localization of a user in the building nor recommendations based onthe location of the user is possible. Also, APs are installed in placeswhere the interference of APs from components of the building isminimized, APs may be easily connected, and the aesthetic appearance ofthe interior is not spoiled by the installation of APs, rather thanbeing installed at regular distances. Furthermore, the locations of APsmay often change due to the failure and replacement thereof. Therefore,even if the locations of APs in a building are detected in advance,additional effort to maintain the information is required.

The present invention detects the probabilistic locations of APs basedon information collected from multiple user terminals even if there isno information about the locations of APs in a building, and proposes anindoor localization technique based on the information about the APs.Also, based on this indoor localization technique, the present inventionintends to disclose a technique for recommending locations according touser preference and the current location of a user when the user entersa certain level of a building that has multiple levels.

As a related art of the present invention, there is Korean PatentApplication Publication No. 10-2011-0133337, disclosed at Dec. 12, 2011and titled “method and apparatus for providing advertisement based onposition information of subscriber”.

DISCLOSURE Technical Problem

An object of the present invention is to realize a positioning serviceusing the fingerprints of access points by enabling indoor positioningeven if there is no information about the access points in a building.

Also, another object of the present invention is to detect the locationof a recommendation target user through indoor positioning and torecommend locations according to user preference, whereby therecommendation target user may conveniently and quickly move in thebuilding.

Technical Solution

In order to accomplish the above object, a device for recommending alocation according to the present invention includes: an access pointlocation detection unit for detecting locations of one or more accesspoints by collecting fingerprints for the one or more access pointsinstalled in the building from terminals of multiple users; a storelocation detection unit for detecting locations of stores in thebuilding using the fingerprints and payment histories of the multipleusers; a metamap generation unit for generating a metamap by integratingthe locations of the one or more access points and the locations of thestores with an indoor plan of the building; a user path estimation unitfor estimating a meta-path of a recommendation target user using thefingerprints obtained from a terminal of the recommendation target user;and a location recommendation unit for recommending a location to therecommendation target user based on the meta-path, using the metamap andpreference information of the recommendation target user.

The store location detection unit may detect locations of one or moreadjacent stores located in a range predetermined based on each of theone or more access points among the stores, using a difference between atime when each of the fingerprints is obtained and a payment timeincluded in the payment histories.

The store location detection unit may detect the locations of the one ormore adjacent stores when the difference between the time when each ofthe fingerprints is obtained and the payment time included in thepayment histories falls within a predetermined reference difference.

The locations of the one or more adjacent stores may be relativelocations to an access point corresponding to the one or more adjacentstores, the access point being selected from the one or more accesspoints.

When the number of the access points corresponding to the one or moreadjacent stores is two or more, the store location detection unit maycorrect the locations of the one or more adjacent stores using relativelocations to each of the two or more access points corresponding to theone or more adjacent stores.

The device for recommending a location may further include a planacquisition unit for obtaining the indoor plan on which at least one ofan area and a length of each section of the building is electronicallymarked.

The plan acquisition unit may overlap at least one of tenant informationof the stores corresponding to the indoor plan and category informationfor the stores obtained using the tenant information on the indoor plan.

The plan acquisition unit may update the tenant information using thepayment histories.

The location recommendation unit may recommend a location of arecommended store corresponding to the preference information to therecommendation target user using the category information, therecommended store being selected from the one or more adjacent storescorresponding to a current location of the recommendation target user onthe meta-path.

The location recommendation unit may recommend the location of therecommended store located in a moving direction defined by the meta-pathto the recommendation target user.

The device for recommending a location may further include a discountvoucher provision unit for transmitting a discount voucher correspondingto the recommended store to the terminal of the recommendation targetuser.

The access point location detection unit may detect a number of the oneor more access points installed in the building using the fingerprints.

The store location detection unit may detect a level on which the storesare located using at least one of periodically collected store locationinformation and user comment information collected on web.

The device for recommending a location may further include a paymentinformation acquisition unit for obtaining the payment histories of themultiple users.

A method for recommending a location according to the present inventionincludes: detecting locations of one or more access points by collectingfingerprints for the one or more access points installed in the buildingfrom terminals of multiple users; detecting locations of stores in thebuilding using the fingerprints and payment histories of the multipleusers; generating a metamap by integrating the locations of the one ormore access points and the locations of the stores with an indoor planof the building; and estimating a meta-path of a recommendation targetuser using the fingerprints obtained from a terminal of therecommendation target user and recommending a location to therecommendation target user based on the meta-path, using the metamap andpreference information of the recommendation target user.

Detecting the locations of the stores comprises calculating a differencebetween a time when each of the fingerprints is obtained and a paymenttime included in the payment histories, and locations of one or moreadjacent stores located in a range predetermined based on each of theone or more access points among the stores may be detected using thedifference between the time when each of the fingerprints is obtainedand the payment time included in the payment histories.

The locations of the one or more adjacent stores may be relativelocations to an access point corresponding to the one or more adjacentstores, the access point being selected from the one or more accesspoints.

The method for recommending a location further includes obtaining theindoor plan on which at least one of an area and a length of eachsection of the building is electronically marked, wherein obtaining theindoor plan may be configured to overlap at least one of tenantinformation of the stores corresponding to the indoor plan and categoryinformation for the stores obtained using the tenant information on theindoor plan.

Recommending the location may be configured to recommend a location of arecommended store corresponding to the preference information to therecommendation target user using the category information, therecommended store being selected from the one or more adjacent storescorresponding to a current location of the recommendation target user onthe meta-path.

Advantageous Effects

According to the present invention, when a positioning service usingfingerprints of access points installed in a building is used, even ifthere is no infrastructure information about the access points of thebuilding, the positioning service using indoor positioning may beprovided by using access point information collected from multiple userterminals.

Also, the present invention recommends the locations of preferable shopsand stores in a building by detecting the current location and themeta-path of a user who enters the building, using fingerprints ofaccess points, whereby the user may move in the building convenientlyand quickly.

DESCRIPTION OF DRAWINGS

FIG. 1 is a view illustrating a system for recommending locations in abuilding, according to an embodiment of the present invention;

FIG. 2 is a block diagram illustrating a device for recommendinglocations in a building, according to an embodiment of the presentinvention;

FIGS. 3A and 3B are views illustrating the distribution of the relativelocations of stores based on access points, according to an embodimentof the present invention;

FIGS. 4A and 4B are views illustrating the probabilistic distribution ofthe locations of a single access point and a single store, according toan embodiment of the present invention;

FIGS. 5A and 5B are views illustrating the probabilistic distribution ofthe locations of multiple access points and a single store, according toan embodiment of the present invention;

FIG. 6 is a view illustrating the relationship between the distancesbetween a store and multiple access points, according to an embodimentof the present invention;

FIG. 7 is a view illustrating the meta-path of a recommendation targetuser on a metamap, according to an embodiment of the present invention;and

FIG. 8 is a flowchart illustrating a method for recommending locationsin a building, according to an embodiment of the present invention.

BEST MODE

The present invention will be described in detail below with referenceto the accompanying drawings. Repeated descriptions and descriptions ofknown functions and configurations which have been deemed to make thegist of the present invention unnecessarily obscure will be omittedbelow. It should be noted that the same reference numerals are used todesignate the same or similar elements throughout the drawings.

Terms or words used in this specification and claims should not beinterpreted according to typical or dictionary meaning, but have to beinterpreted as the meaning and concept adaptive to the technical idea ofthe present invention based on a principle that an inventor may properlydefine the concept of the terms in order to explain the presentinvention in the best way. Therefore, embodiments disclosed in thisspecification and configurations illustrated in the drawings are merelypreferred embodiments of the present invention and do not fully describethe technical idea of the present invention, thus there may be variousequivalents and alterations replacing them at a filing date of thepresent application. Also, it will be understood that, although theterms first, second, etc. may be used herein to describe variouselements, these elements should not be limited by these terms. Theseterms are only used to distinguish one element from another.

FIG. 1 is a view illustrating a system for recommending locations in abuilding, according to an embodiment of the present invention.

Referring to FIG. 1, a system for recommending locations in a building,according to an embodiment of the present invention, may include alocation recommendation device 100, a recommendation target user'sterminal 110, multiple users' terminals 120, and the Internet 130.

The location recommendation device 100 may detect the locations of oneor more access points by collecting fingerprints for the one or moreaccess points installed in a building from the terminals 120 of multipleusers. The technique using the fingerprints is a method in which anindoor magnetic field map is generated in advance and a location isestimated by comparing a value measured by the geometric sensor of auser terminal with the information in the map.

In this case, the number of the one or more access points installed inthe building may be detected using the fingerprints. For example, whenmultiple users move around each level of a specific building, thefingerprints of access points may be collected through communicationwith the access points installed in various locations. In the case of anaccess point, there may be interference between levels according to thebuilding, but the number of access points present in a building may bedetected when information from multiple users is accumulated.

Also, the location recommendation device 100 may acquire the paymenthistories of multiple users. For example, payment transactioninformation generated in a store may be acquired using a mobile cardmounted in a user terminal, or payment histories may be acquired througha service in which both smart phone subscriber information and cardpayment histories of the subscriber are obtained.

Also, the location recommendation device 100 may detect the locations ofstores in a building using the fingerprints collected from the terminals120 of multiple users and payment histories of the multiple users.

In this case, using the difference between the time at which afingerprint is acquired and the payment time included in a paymenthistory, it is possible to detect the locations of one or more adjacentstores located in a range predetermined based on each of one or moreaccess points. For example, if the fingerprint of the access point A isacquired from the terminal of a specific user at 2:50 p.m. and thespecific user made a payment at the store B at 3 p.m., the store B maybe located on a concentric circle that corresponds to a 10-minutewalking distance from the access point A.

In this case, if the difference between the time when the fingerprint isacquired and the payment time falls within a predetermined referencedifference, the locations of the one or more adjacent stores may bedetected. For example, the reference difference is set by consideringthe average stride of adults, the size of the building, and the like,and among information generated from multiple users, only informationsatisfying the condition in which the difference between the time when afingerprint is acquired and the time when payment is made at a specificstore falls within the predetermined reference difference may be used todetect the location.

In this case, the locations of the one or more adjacent stores may berelative locations based on an access point corresponding to the one ormore adjacent stores, the access point being selected from the one ormore access points.

Here, if there are two or more access points corresponding to the one ormore adjacent stores, the locations of the one or more adjacent storesmay be corrected using the relative locations to each of the two or moreaccess points corresponding to the one or more adjacent stores. Forexample, an area in which a specific store is more likely to existaccording to the payment history may be determined based on the accesspoints. If, based on two access points, areas with high probability thata specific store exists therein overlap each other, it may be detectedhow far the specific store is located from each of the two accesspoints. When the relationship between a specific store and multipleaccess points is calculated according to probabilities through such amethod, the range encompassing the location of the specific store may beprecisely determined.

In this case, which level the stores are located on may be detectedusing at least one of periodically collected store location informationand user comments collected on the Web. Using the fingerprints of accesspoints acquired from the terminals 120 of multiple users, the locationsor the number of the access points in a building can be detected, but itis difficult to detect which level each of the access points is locatedon. The level on which the access points are located may be accuratelydetected by overlapping the locations of all the access points on theindoor map in advance, but it is difficult to apply this method to aplurality of various buildings in practice. Therefore, the level may bedetected using the store location information periodically collected bypeople, or the level may be estimated by intensively analyzing keywordssuch as ‘building name’, ‘location name’, ‘store name’, and ‘level’included in user comments collected on the Internet 130. For example,information indicating that the store ‘Gimbab Sarang’ is located on the‘10th’ floor of the ‘COEX’ building may be obtained, and the level maybe detected by collecting user comments on the Internet, such as “I wentto ‘COEX’ and had lunch at ‘Gimbab Sarang’”, or “The food at ‘GimbabSarang’ on the ‘10th floor’ tastes good”.

Also, the location recommendation device 100 may generate a metamap byintegrating the locations of one or more access points and stores withthe indoor plan of a building. For example, the metamap may berepresented in the form of a graph showing the relationship between astore and an access point by continuously marking the point at which theprobability that the specific store exists is the highest, based on thespecific access point. In this case, because of high mobility betweenlevels or the locations of access points, the relative location of astore that is located on a level different from the level on which theaccess point is located may be calculated. However, the effect ofinterference between levels may be decreased by data collected frommultiple users, and a store connected to a certain number of accesspoints is difficult to connect to another access point. Therefore, whenan access point and a store are not connected to each other, the storeand the access point are assumed to be on different levels, and they arealso assumed not to be within interference range.

Also, the location recommendation device 100 may estimate the meta-pathof a recommendation target user using the fingerprints acquired from theterminal 110 of the recommendation target user. For example, using thefingerprints acquired from the terminal 110 of the recommendation targetuser, which access point is located near the user may be detected, andthe meta-path showing which access points the user has passed throughmay be estimated. In reality, whether the user is moving to the eastside or the west side or whether the user is moving along a specificcorridor may be unknown, but it is possible to detect which stores areclose to the area that the user is moving to.

Also, the location recommendation device 100 may obtain an indoor planon which at least one of the area and the length of each section of abuilding is electronically marked.

In this case, at least one of the information about the tenants in thestores corresponding to the indoor plan and the category information foreach of the stores, acquired using the tenant information, may beoverlaid on the indoor plan. For example, the type of business isanalyzed based on the name or the business registration number of thestore overlaid on the indoor plan, whereby the category information, forexample, whether the store is a restaurant or a clothing store, may bedetected. This information may be detected based on business nameregistration information, and may additionally be detected by collectingcontent on the Internet 130. Also, information about product lines orthe types of goods sold by each of the stores may be accumulated bycollecting information. Also, it is possible to induce a store, whichintends to provide recommendations for a specific user group in abuilding, to voluntarily provide information through a system.

In this case, the tenant information may be updated using paymenthistories. For example, when payment histories for a store that waslocated in a specific location A have not been made for a long time, orwhen the type of goods paid for, checked via the payment histories, haschanged, the tenant information may be updated to reflect that the storethat was in A has gone or the business type of the store in A haschanged. Also, because a large-scale store such as a movie theater or asuperstore is less likely to disappear in a short time, informationabout which level such a store is located on is collected, and then thelocation may be fixed.

Also, using a metamap and information about the preferences of arecommendation target user, the location recommendation device 100 mayrecommend locations to the recommendation target user based on themeta-path. For example, if the user preference information includes thename of a specific store, the location of the specific store, locatednear the meta-path of the user, may be recommended.

In this case, using the category information, the location of arecommended store corresponding to the preference information, selectedfrom one or more adjacent stores corresponding to the current locationof a recommendation target user on the meta-path, may be recommended tothe recommendation target user. For example, if the information aboutthe preferences of the recommendation target user includes informationabout clothes or accessories such as bags, the location of an adjacentstore, corresponding to the category of clothes or accessories, may berecommended by being selected from the one or more adjacent stores.

In this case, the location of a recommended store located along themeta-path in the moving direction may be recommended to the user. Forexample, when the meta-path of the recommendation target user moves fromthe east side to the west side, a recommended store corresponding to theuser preference information may be recommended by being selected fromone or more adjacent stores located in the area in which the user willarrive, namely, at least one adjacent store located on the west side.

Also, the location recommendation device 100 may transmit discountvouchers corresponding to the recommended store to the terminal 110 ofthe recommendation target user. For example, when the recommendationtarget user is near a theater in a relevant building, a discount voucherfor a movie is transmitted to the terminal 110 of the recommendationtarget user, or vouchers for restaurants located in the relevantbuilding or a nearby building may be transmitted by detecting paymenthistories in the theater in real time.

The terminal 110 of the recommendation target user transmits thefingerprints obtained by accessing at least one access point to thelocation recommendation device 100, whereby the location recommendationdevice 100 may estimate the meta-path of the recommendation target user.Then, the location recommendation device 100 may determine a recommendedlocation based on the meta-path of the recommendation target user, andmay transmit the recommended location to the terminal 110 of therecommendation target user.

The terminals 120 of multiple users may access one or more access pointsinstalled in a building, and may obtain fingerprints for each of theaccess points. The locations or the number of access points installed inthe building may be determined using the fingerprints obtained from theterminals 120 of multiple users. Also, based on the access points, therelative locations of the stores located and operated inside thebuilding may be detected by acquiring payment histories from theterminals 120 of multiple users.

The Internet 130 may provide information that is necessary for detectingwhich level the stores are located on in the building because it isdifficult to obtain such information from the fingerprints of accesspoints and payment histories obtained from the terminals 120 of multipleusers. For example, keywords corresponding to the building name, thename of the store in the building, or the level may be obtained frominformation included in user comments, whereby the level on which thestores are located in the building may be obtained. Also, in order todetect category information for the stores, content collected on theInternet 130 may be used.

When the system for recommending locations in a building, describedabove, is used, a location recommendation service through indoorpositioning may be provided even in a building in which noinfrastructure information is constructed for access points or stores ofthe building.

FIG. 2 is a block diagram illustrating a device 100 for recommendinglocations in a building, according to an embodiment of the presentinvention.

Referring to FIG. 2, the device 100 for recommending locations in abuilding may include an access point location detection unit 210, astore location detection unit 220, a metamap generation unit 230, a userpath estimation unit 240, a location recommendation unit 250, a paymentinformation acquisition unit 260, a plan acquisition unit 270, and adiscount voucher provision unit 280.

The access point location detection unit 210 may detect the locations ofone or more access points by collecting fingerprints for the one or moreaccess points installed in a building from multiple user terminals. Thetechnique using the fingerprints is a method in which an indoor magneticfield map is generated in advance and a location is estimated bycomparing a value measured by the geometric sensor of a user terminalwith the information in the map.

In this case, the number of the one or more access points installed inthe building may be detected using the fingerprints. For example, whenmultiple users move around each level of a specific building,fingerprints of access points may be collected through communicationwith the access points installed in various locations. In the case of anaccess point, there may be interference between levels according to thebuilding, but the number of access points that are present in a buildingmay be detected when information from multiple users has beenaccumulated.

The store location detection unit 220 may detect the locations of storesin a building using the fingerprints collected from the terminals ofmultiple users and payment histories of the multiple users.

In this case, using the difference between the time when a fingerprintis acquired and the payment time included in a payment history, it ispossible to detect the locations of one or more adjacent stores locatedin a range predetermined based on each of one or more access points. Forexample, if the fingerprint of the access point A is acquired from theterminal of a specific user at 2:50 p.m. and the specific user made apayment at the store B at 3 p.m., the store B may be located on aconcentric circle that indicates 10-minute walking distance from theaccess point A.

In this case, if the difference between the time when the fingerprint isacquired and the payment time falls within a predetermined referencedifference, the locations of the one or more adjacent stores may bedetected. For example, the reference difference is set by consideringthe average stride of adults, the size of the building, and the like,and among information generated from multiple users, only informationsatisfying the condition in which the difference between the time when afingerprint is acquired and the time when payment is made at a specificstore falls within the predetermined reference difference may be usedfor detecting the location.

In this case, the locations of the one or more adjacent stores may berelative locations based on an access point corresponding to the one ormore adjacent stores, the access point being selected from the one ormore access points.

Here, if there are two or more access points corresponding to the one ormore adjacent stores, the locations of the one or more adjacent storesmay be corrected using the relative locations to each of the two or moreaccess points corresponding to the one or more adjacent stores. Forexample, an area in which a specific store is more likely to existaccording to the payment history may be determined based on the accesspoints. If, based on two access points, areas with high probability thata specific store exists therein overlap each other, it may be detectedhow far the specific store is located from the two access points. Whenthe relationship between a specific store and multiple access points iscalculated according to probabilities through such a method, the rangeincluding the location of the specific store may be preciselydetermined.

In this case, which level the stores are located on may be detectedusing at least one of periodically collected store location informationand user comments collected on the Web. Using the fingerprints of accesspoints acquired from the terminals of multiple users, the locations orthe number of the access points in a building can be detected, but it isdifficult to detect which level each of the access points is located on.The level on which the access points are located may be accuratelydetected by overlapping the locations of all the access points on theindoor map in advance, but it is difficult to apply this method to aplurality of various buildings in practice. Therefore, the level may bedetected using the store location information periodically collected bypeople, or the level may be estimated by intensively analyzing keywordssuch as ‘building name’, ‘location name’, ‘store name’, and ‘level’included in user comments collected on the Internet 130. For example,information indicating that the store ‘Gimbab Sarang’ is located on the‘10th’ floor of the ‘COEX’ building may be obtained and the level may bedetected by collecting user comments on the Internet, such as “I went to‘COEX’ and had lunch at ‘Gimbab Sarang’”, or “The food at ‘GimbabSarang’ on the ‘10th floor’ tastes good”.

The metamap generation unit 230 may generate a metamap by integratingthe locations of one or more access points and stores with the indoorplan of a building. For example, the metamap may be represented in theform of a graph showing the relationship between a store and an accesspoint by continuously marking the point at which the probability thatthe specific store exists is the highest based on the specific accesspoint. In this case, because of high mobility between levels or thelocations of access points, the relative location of a store that islocated on a level different from the level on which the access point islocated may be calculated. However, the effect of interference betweenlevels may be decreased by data collected from multiple users, and astore connected to a certain number of access points is difficult toconnect to another access point. Therefore, when an access point and astore are not connected to each other, the store and the access pointare assumed to be on different levels, and they are assumed not to bewithin interference range.

The user path estimation unit 240 may estimate the meta-path of arecommendation target user using the fingerprints acquired from theterminal of the recommendation target user. For example, using thefingerprints acquired from the terminal of the recommendation targetuser, which access point is located near the user may be detected, and ameta-path showing which access points the user has passed through may beestimated. Actually, whether the user is moving to the east side or thewest side or whether the user is moving along a specific corridor cannotbe known, but it is possible to detect which stores are close to thearea that the user is moving to.

Using a metamap and information about the preferences of arecommendation target user, the location recommendation unit 250 mayrecommend locations to the recommendation target user based on themeta-path. For example, if the user preference information includes thename of a specific store, the location of the specific store locatednear the meta-path of the user may be recommended.

In this case, using the category information, the location of arecommended store corresponding to the preference information, selectedfrom one or more adjacent stores corresponding to the current locationof a recommendation target user on the meta-path, may be recommended tothe recommendation target user. For example, if the information aboutthe preferences of the recommendation target user includes informationabout clothes or accessories such as bags, the location of an adjacentstore corresponding to the category of clothes or accessories may berecommended by being selected from the one or more adjacent stores.

In this case, the location of a recommended store located along themeta-path in the moving direction may be recommended to the user. Forexample, when the meta-path of the recommendation target user moves fromthe east side to the west side, a recommended store corresponding to theuser preference information may be recommended by being selected fromone or more adjacent stores located in the area in which the user willarrive, namely, at least one adjacent store located on the west side.

The payment information acquisition unit 260 may acquire the paymenthistories of multiple users. For example, payment transactioninformation generated in a store may be acquired using a mobile cardmounted in a user terminal, or payment histories may be acquired througha service in which both smart phone subscriber information and cardpayment histories of the subscriber are obtained.

The plan acquisition unit 270 may acquire an indoor plan on which atleast one of the area and the length of each section of a building iselectronically marked.

In this case, at least one of the tenant information of the storescorresponding to the indoor plan and the category information for eachof the stores, acquired using the tenant information, may be overlaid onthe indoor plan. For example, the type of business is analyzed based onthe name or the business registration number of the store overlaid onthe indoor plan, whereby the category information, for example, whetherthe store is a restaurant or a clothing store, may be detected. Thisinformation may be detected based on business name registrationinformation, and may additionally be detected by collecting content onthe Internet. Also, information about product lines or the types ofgoods sold by each of the stores may be accumulated by collectinginformation. Also, it is possible to induce a store, which intends toprovide recommendations for a specific user group in a building, tovoluntarily provide information through a system.

In this case, the tenant information may be updated using paymenthistories. For example, when payment histories for the store that waslocated at a specific location A have not been made for a long time orwhen the type of goods paid for, checked via the payment histories, haschanged, the tenant information may be updated to reflect that the storethat was in A has gone or that the business type of the store in A haschanged. Also, because a large-scale store such as a movie theater or asuperstore is less likely to disappear in a short time, afterinformation about which level such a store is located on is collected,the location may be fixed.

The discount voucher provision unit 280 may transmit discount voucherscorresponding to the recommended store to the terminal of therecommendation target user. For example, when the recommendation targetuser is near a theater in a relevant building, a discount voucher for amovie is transmitted to the terminal of the recommendation target user,or vouchers for restaurants located in the relevant building or a nearbybuilding may be transmitted by detecting payment histories in thetheater in real time.

Because a location recommendation service is provided using theabove-mentioned location recommendation device 100 even in a building inwhich no infrastructure information is gathered in advance, users usingthe service may conveniently and quickly detect the locations in thebuilding.

FIGS. 3A and 3B are views illustrating the distribution of the relativelocations of stores based on access points, according to an embodimentof the present invention.

Referring to FIGS. 3A and 3B, FIG. 3A shows a result for a single user,and the locations 330 in which a store is likely to exist based on thefirst access point 310 are marked within the probability distributionrange 320 of the first access point 310. Also, when the distribution ofFIG. 3A is accumulated by obtaining both the fingerprints of the firstaccess point 310 and the time when payment is made at the store frommultiple users, locations 340 having a high probability that the storeexists therein may be generated, and thus the range may be narroweddown.

FIGS. 4A and 4B are views illustrating the probabilistic distribution ofthe locations of a single access point and a single store, according toan embodiment of the present invention.

Referring to FIGS. 4A and 4B, the distribution of the relative locationsof the store illustrated in FIGS. 3A and 3B is illustrated according toprobability. In FIG. 4A, if the concentric circles generated based on anaccess point indicate the location ranges of a store, the store is morelikely to exist closer to the store location range 412 than to the storelocation range 411. Conversely, in FIG. 4B, the probability that thestore exists in the store location range 413 is the highest. The reasonwhy the probabilistic distribution of the locations of the store isdifferent is that payment may not be made near the access point. If thelocations closest to the access point shown in FIG. 4A are excluded, itmay be considered that the store is highly likely to exist at a certaindistance apart from the access point, as shown in FIG. 4B.

FIGS. 5A and 5B are views illustrating the probabilistic distribution ofthe locations of multiple access points and a single store, according toan embodiment of the present invention.

Referring to FIGS. 5A and 5B, the bands in the form of a concentriccircle generated based on the first access point and the second accesspoint may indicate the areas in which a store is likely to exist basedon the access points. As shown in FIG. 5A, when the information for thetwo access points is overlaid, the absolute location of the store isunknown but the location information 511 and 512, indicating thelocation in which the store is likely to exist based on the two accesspoints, namely, information indicating how far the single store islocated from the two access points, may be checked.

FIG. 5B acquires the location information 531 indicating a location witha high probability that a certain store exists there by adding an areabased on the third access point to the configuration illustrated in FIG.5A. In this case, although the pieces of location information 521, 522,and 523, indicating locations with a possibility that a store existsthere based on the three access points, have a lower possibilitycompared to the location information 531, indicating a location with ahigh probability that the store exists there, they still have somepossibility that the store exists there. Using such information, thepresent invention may detect which access point is located near a userand which stores are located near the access point rather than the exactlocation of an access point or a store.

FIG. 6 is a view illustrating the relationship between the distancesbetween a store and multiple access points, according to an embodimentof the present invention.

Referring to FIG. 6, the relationship between the distances between astore and multiple access points may be represented using a metamap. Bycontinuously marking the point at which the probability that a specificstore exists is the highest based on a specific access point, therelationship between the store and the access point may be representedin the form of a graph, as shown in FIG. 6.

In this case, it is difficult to obtain information about a shadow areain which access point information cannot be obtained. Also, for a fixednumber of access points in a building, even if the effect ofinterference between levels may be decreased using data collected frommultiple users and a store connected to a certain number of accesspoints is difficult to connect to another access point, the relativelocation of a store that is located on a level different from the levelon which the access point is located may be calculated because of highmobility between levels or the locations of access points.

In this case, excluding payment histories, when a direction is drawn byfollowing the sequence of access points accessed by a user, thedirection of access points accessed by multiple users may be obtained.For example, when data of multiple users, in which access to the seventhaccess point immediately follows access to the sixth access point, canbe found but the sequence linking the third access point to the fifthaccess point has relatively low frequency, it may be confirmed that thepoint linking two separate groups is the point linking the sixth accesspoint and the seventh access point. Through such a method, a path thatlinks separate groups may be found.

FIG. 7 is a view illustrating the meta-path of a recommendation targetuser on a metamap, according to an embodiment of the present invention.

Referring to FIG. 7, the meta-path of a recommendation target user on ametamap, according to an embodiment of the present invention, may berepresented as a location relative to the locations of access points andstores displayed on the metamap.

Through the meta-path of a recommendation target user, it may bedetected which access point is near the user or which access points theuser has passed through. Whether the user is moving to the east side orthe west side on the relevant level, or whether the user is moving alonga specific corridor may not be checked using the meta-path on themetamap, but which stores are close to the area in which the user ismoving may be detected. Therefore, vouchers for nearby stores may beprovided, or products may be recommended based on user preferencedetected for individuals.

FIG. 8 is a flowchart showing a method for recommending locations in abuilding, according to an embodiment of the present invention.

Referring to FIG. 8, the method for recommending locations in abuilding, according to an embodiment of the present invention, maydetect the locations of one or more access points by collectingfingerprints for the one or more access points installed in a buildingfrom multiple user terminals at step S810. The technique usingfingerprints is a method in which an indoor magnetic field map isgenerated in advance and a location is estimated by comparing a valuemeasured by the geometric sensor of a user terminal with the informationin the map.

In this case, the number of the one or more access points installed inthe building may be detected using the fingerprints. For example, whenmultiple users move around each level of a specific building,fingerprints of access points may be collected through communicationwith the access points installed in various locations. In the case of anaccess point, there may be interference between levels according to thebuilding, but the number of access points present in a building may bedetected when information from multiple users has been accumulated.

Also, the method for recommending locations in a building, according toan embodiment of the present invention, may detect the locations ofstores in a building using the fingerprints collected from the terminalsof multiple users and payment histories of the multiple users at stepS820.

In this case, using the difference between the time when a fingerprintis acquired and the payment time included in a payment history, it ispossible to detect the locations of one or more adjacent stores locatedin a range predetermined based on each of one or more access points. Forexample, if the fingerprint of the access point A is acquired from theterminal of a specific user at 2:50 p.m. and the specific user made apayment at the store B at 3 p.m., the store B may be located on aconcentric circle that indicates 10-minute walking distance from theaccess point A.

In this case, if the difference between the time at which thefingerprint is acquired and the payment time falls within apredetermined reference difference, the locations of the one or moreadjacent stores may be detected. For example, the reference differenceis set by considering the average stride of adults, the size of thebuilding, and the like, and among information generated from multipleusers, only the information satisfying the condition in which thedifference between the time when a fingerprint is acquired and the timewhen payment is made at a specific store falls within the predeterminedreference difference may be used to detect the location.

In this case, the locations of the one or more adjacent stores may berelative locations based on an access point corresponding to the one ormore adjacent stores, the access point being selected from the one ormore access points.

Here, if there are two or more access points corresponding to the one ormore adjacent stores, the locations of the one or more adjacent storesmay be corrected using the relative locations to each of the two or moreaccess points corresponding to the one or more adjacent stores. Forexample, an area in which a specific store is more likely to existaccording to the payment history may be determined based on the accesspoints. If, based on two access points, areas with high probability thata specific store exists therein overlap each other, it may be detectedhow far the specific store is located from the two access points. Whenthe relationship between a specific store and multiple access points iscalculated according to probabilities through such a method, the rangeincluding the location of the specific store may be preciselydetermined.

In this case, which level the stores are located on may be detectedusing at least one of periodically collected store location informationand user comments collected on the Web. Using the fingerprints of accesspoints acquired from the terminals of multiple users, the locations orthe number of the access points in a building can be detected, but it isdifficult to detect which level each of the access points is located on.The level on which the access points are located may be accuratelydetected by overlapping the locations of all the access points on theindoor map in advance, but it is difficult to apply this method to aplurality of various buildings in practice. Therefore, the level may bedetected using the store location information periodically collected bypeople, or the level may be estimated by intensively analyzing keywordssuch as ‘building name’, ‘location name’, ‘store name’, and ‘level’included in user comments collected on the Internet 130. For example,information indicating that the store ‘Gimbab Sarang’ is located on the‘10th’ floor of the ‘COEX’ building may be obtained and the level may bedetected by collecting user comments on the Internet, such as “I went to‘COEX’ and had lunch at ‘Gimbab Sarang’”, or “The food at ‘GimbabSarang’ on the ‘10th floor’ tastes good”.

Also, the method for recommending locations in a building, according toan embodiment of the present invention, may generate a metamap byintegrating the locations of one or more access points and stores withthe indoor plan of a building at step S830. For example, the metamap maybe represented in the form of a graph showing the relationship between astore and an access point by continuously marking the point at which theprobability that the specific store exists is the highest based on thespecific access point. In this case, because of high mobility betweenlevels or the locations of access points, the relative location of astore that is located on a level different from the level on which theaccess point is located may be calculated. However, the effect ofinterference between levels may be decreased by data collected frommultiple users, and a store connected to a certain number of accesspoints is difficult to connect to another access point. Therefore, whenan access point and a store are not connected to each other, the storeand the access point are assumed to be on different levels, and they areassumed not to be within interference range.

Also, the method for recommending locations in a building, according toan embodiment of the present invention, may estimate the meta-path of arecommendation target user using the fingerprints acquired from theterminal of the recommendation target user, and may recommend locationsto the recommendation target user based on the meta-path, using themetamap and the information about the preferences of the recommendationtarget user at step S840. For example, using the fingerprints acquiredfrom the terminal of the recommendation target user, which access pointis located near the user may be detected, and the meta-path showingwhich access points the user has passed through may be estimated.Actually, whether the user is moving to the east side or the west sideor whether the user is moving along a specific corridor cannot be known,but it is possible to detect which stores are close to the area that theuser is moving to. Also, for example, if the user preference informationincludes the name of a specific store, the location of the specificstore located near the meta-path of the user may be recommended.

In this case, using the category information, the location of arecommended store corresponding to the preference information, selectedfrom one or more adjacent stores corresponding to the current locationof a recommendation target user on the meta-path, may be recommended tothe recommendation target user. For example, if the information aboutthe preferences of the recommendation target user includes informationabout clothes or accessories such as bags, the location of an adjacentstore corresponding to the category of clothes or accessories may berecommended by being selected from the one or more adjacent stores.

In this case, the location of a recommended store located along themeta-path in the moving direction may be recommended to the user. Forexample, when the meta-path of the recommendation target user leads fromthe east side to the west side, a recommended store corresponding to theuser preference information may be recommended by being selected fromone or more adjacent stores located in the area in which the user willarrive, namely, at least one adjacent store located in the west side.

Also, although not illustrated in FIG. 8, the method for recommendinglocations in a building, according to an embodiment of the presentinvention, may acquire payment histories of multiple users. For example,payment transaction information generated in a store may be acquiredusing a mobile card mounted in a user terminal, or payment histories maybe acquired through a service in which both smart phone subscriberinformation and card payment histories of the subscriber are obtained.

Also, although not illustrated in FIG. 8, the method for recommendinglocations in a building, according to an embodiment of the presentinvention, may obtain an indoor plan on which at least one of the areaand the length of each section of a building is electronically marked.

In this case, at least one of the tenant information of the storescorresponding to the indoor plan and the category information for eachof the stores, acquired using the tenant information, may be overlaid onthe indoor plan. For example, the type of business is analyzed based onthe name or the business registration number of the store overlaid onthe indoor plan, whereby the category information, for example, whetherthe store is a restaurant or a clothing store, may be detected. Thisinformation may be detected based on business name registrationinformation, and may additionally be detected by collecting content onthe Internet. Also, information about product lines or the type of goodssold by each of the stores may be accumulated by collecting information.Also, it is possible to induce a store, which intends to providerecommendations for a specific user group in a building, to voluntarilyprovide information through a system.

In this case, the tenant information may be updated using paymenthistories. For example, when payment histories for a store that waslocated in a specific location A have not been made for a long time orwhen the type of goods paid for, checked via the payment histories, haschanged, the tenant information may be updated to reflect that the storethat was in A has gone or that the type of business in the store in Ahas changed. Also, because a large-scale store such as a movie theateror a superstore is less likely to disappear in a short time, informationabout the level such a store is located on is collected, and thelocation may then be fixed.

Also, although not illustrated in FIG. 8, the method for recommendinglocations in a building, according to an embodiment of the presentinvention, may transmit discount vouchers corresponding to therecommended store to the terminal of the recommendation target user. Forexample, when the recommendation target user is near a theater in arelevant building, a discount voucher for a movie is transmitted to theterminal of the recommendation target user, or vouchers for restaurantslocated in the relevant building or a nearby building may be transmittedby detecting payment histories in the theater in real time.

Using the method for recommending locations in a building, the locationof a recommendation target user is detected through indoor positioningand locations are recommended according to user preference, whereby therecommendation target user may conveniently and quickly move in abuilding.

The method for recommending locations in a building, according to thepresent invention, may be implemented as a program that can be executedby various computer means. In this case, the program app may be recordedon a computer-readable storage medium. The computer-readable storagemedium may include program instructions, data files, and data structuressolely or in combination. Program instructions recorded on the storagemedium may have been specially designed and configured for the presentinvention, or may be known to or available to those who have ordinaryknowledge in the field of computer software. Examples of thecomputer-readable storage medium include all types of hardware devicesspecially configured to record and execute program instructions, such asmagnetic media, such as a hard disk, a floppy disk, and magnetic tape,optical media, such as CD-ROM and a DVD, magneto-optical media, such asa floptical disk, ROM, random access memory (RAM), and flash memory.Examples of the program instructions include machine code, such as codecreated by a compiler, and high-level language code executable by acomputer using an interpreter. The hardware devices may be configured tooperate as one or more software modules in order to perform theoperation of the present invention, and vice versa.

As described above, a device for recommending locations in a buildingusing fingerprints of access points and a method using the device arenot limited and applied to the configurations and operations of theabove-described embodiments, but all or some of the embodiments may beselectively combined and configured so that the embodiments may bemodified in various ways.

INDUSTRIAL APPLICABILITY

According to the present invention, the locations of access points aredetected by collecting fingerprints for the access points from terminalsof multiple users, the locations of stores in a building are detected byusing the fingerprints and payment histories of multiple users, andlocations based on a meta-path may be recommended to a recommendationtarget user using a metamap generated by integrating the locations ofthe access points and stores with the indoor plan of the building.Furthermore, in providing a location recommendation service, the servicemay be provided without organizing information about the infrastructureof the building in advance, and it is easy to update information inresponse to the change of stores or the change of access pointlocations, which may be caused by the replacement of equipment.Therefore, material and time resources required for constructinginformation in advance for the location recommendation service may besaved.

The invention claimed is:
 1. A device for recommending a location in abuilding, comprising: an access point location detector configured todetect locations of one or more access points by collecting fingerprintsfor the one or more access points installed in the building fromterminals of multiple users; a store location detector configured todetect locations of stores in the building using the fingerprints andpayment histories of the multiple users; a metamap generator configuredto generate a metamap by integrating the locations of the one or moreaccess points and the locations of the stores with an indoor plan of thebuilding; and a location recommender configured to recommend a locationto a recommendation target user using the metamap, wherein the storelocation detector is configured to detect locations of one or moreadjacent stores located in a predetermined range based on each of theone or more access points among the stores when a difference between atime when each of the fingerprints is obtained and a payment timeincluded in the payment histories falls within a predetermined referencedifference, wherein the predetermined reference difference is set by anaverage stride of adults and a size of the building.
 2. The device ofclaim 1, wherein the locations of the one or more adjacent stores arerelative locations to an access point corresponding to the one or moreadjacent stores, the access point being selected from the one or moreaccess points.
 3. The device of claim 2, wherein when a number of theaccess points corresponding to the one or more adjacent stores is two ormore, the store location detector is configured to correct the locationsof the one or more adjacent stores using relative locations to each ofthe two or more access points corresponding to the one or more adjacentstores.
 4. The device of claim 1, wherein the store location detector isconfigured to detect a level on which the stores are located using atleast one of periodically collected store location information and usercomment information collected on web.
 5. The device of claim 1, whereinthe metamap generator is configured to generate the metamap which isrepresented in a form of a graph showing a relationship between a storeand an access point by marking the point at which the probability thatthe specific store exists is the highest based on the specific accesspoint.
 6. The device of claim 1, wherein the device further comprises: aplan receiver configured to obtain the indoor plan on which at least oneof an area and a length of each section of the building iselectronically marked.
 7. The device of claim 6, wherein the planreceiver is configured to overlap at least one of tenant information ofthe stores corresponding to the indoor plan and category information forthe stores obtained using the tenant information on the indoor plan. 8.The device of claim 7, the category information is detected based on aname or a business registration number of the store overlaid on theindoor plan.
 9. The device of claim 7, wherein the plan receiver isconfigured to update the tenant information using the payment histories.10. The device of claim 7, wherein the location recommender isconfigured to recommend a location of a recommended store correspondingto the preference information to the recommendation target user usingthe category information, the recommended store being selected from theone or more adjacent stores corresponding to a current location of therecommendation target user on the meta-path.
 11. The device of claim 10,wherein the location recommender is configured to recommend the locationof the recommended store located in a moving direction defined by themeta-path to the recommendation target user.
 12. The device of claim 1,wherein the device further comprises: a payment information receiverconfigured to obtain the payment histories of the multiple users. 13.The device of claim 12, wherein the payment information receiver isconfigured to obtain the payment histories through a service in whichboth smart phone subscriber information and card payment histories ofthe subscriber.
 14. A method for recommending a location in a building,comprising: detecting locations of one or more access points bycollecting fingerprints for the one or more access points installed inthe building from terminals of multiple users; detecting locations ofstores in the building using the fingerprints and payment histories ofthe multiple users; generating a metamap by integrating the locations ofthe one or more access points and the locations of the stores with anindoor plan of the building; and recommending a location to arecommendation target user using the metamap, when a difference betweena time when each of the fingerprints is obtained and a payment timeincluded in the payment histories falls within a predetermined referencedifference, detecting locations of one or more adjacent stores locatedin a predetermined range based on each of the one or more access pointsamong the stores, wherein the predetermined reference difference is setby an average stride of adults and a size of the building.
 15. Themethod of claim 14, further comprising, acquiring payment histories ofmultiple users, wherein the payment histories are obtained through aservice in which both smart phone subscriber information and cardpayment histories of the subscriber.
 16. The method of claim 14, furthercomprising, obtaining the indoor plan on which at least one of an areaand a length of each section of the building is electronically marked,wherein obtaining the indoor plan is configured to overlap at least oneof tenant information of the stores corresponding to the indoor plan andcategory information for the stores obtained using the tenantinformation on the indoor plan.
 17. A non-transitory computer-readablestorage medium storing a program for implementing a method forrecommending a location in a building, the method comprising: detectinglocations of one or more access points by collecting fingerprints forthe one or more access points installed in the building from terminalsof multiple users; detecting locations of stores in the building usingthe fingerprints and payment histories of the multiple users; generatinga metamap by integrating the locations of the one or more access pointsand the locations of the stores with an indoor plan of the building; andrecommending a location to a recommendation target user using themetamap, when a difference between a time when each of the fingerprintsis obtained and a payment time included in the payment histories fallswithin a predetermined reference difference, detecting locations of oneor more adjacent stores located in a predetermined range based on eachof the one or more access points among the stores, wherein thepredetermined reference difference is set by an average stride of adultsand a size of the building.